Allocating data for storage by utilizing a location-based hierarchy in a dispersed storage network

ABSTRACT

A method for execution by a dispersed storage and task (DST) processing unit includes generating storage allocation data indicating a subset of a plurality of storage units based on storage location hierarchy data and an information dispersal algorithm (IDA) width. A plurality of write requests corresponding to each storage unit in the first subset are generated for transmission via a network, where each of the plurality of write requests includes one of a plurality of encoded slices of a data object.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility patent application claims priority pursuant to35 U.S.C. §119(e) to U.S. Provisional Application No. 62/314,839,entitled “PROCESSING AN ENCODED DATA SLICE IN A DISPERSED STORAGENETWORK”, filed Mar. 29, 2016, which is hereby incorporated herein byreference in its entirety and made part of the present U.S. Utilitypatent application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 10 is a logic diagram of an example of a method of allocating datafor storage by utilizing a location-based hierarchy in accordance withthe present invention; and

FIG. 11 is a schematic block diagram of an embodiment of a decentralizedagreement module in accordance with the present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a dispersed storage and task (DST) execution unit and aset of storage units may be interchangeably referred to as a set of DSTexecution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each managing unit 18 and the integrity processing unit 20 maybe separate computing devices, may be a common computing device, and/ormay be integrated into one or more of the computing devices 12-16 and/orinto one or more of the storage units 36. In various embodiments,computing devices 12-16 can include user devices and/or can be utilizedby a requesting entity generating access requests, which can includerequests to read or write data to storage units in the DSN.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm,Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematicencoding, on-line codes, etc.), a data segmenting protocol (e.g., datasegment size, fixed, variable, etc.), and per data segment encodingvalues. The per data segment encoding values include a total, or pillarwidth, number (T) of encoded data slices per encoding of a data segmenti.e., in a set of encoded data slices); a decode threshold number (D) ofencoded data slices of a set of encoded data slices that are needed torecover the data segment; a read threshold number (R) of encoded dataslices to indicate a number of encoded data slices per set to be readfrom storage for decoding of the data segment; and/or a write thresholdnumber (W) to indicate a number of encoded data slices per set that mustbe accurately stored before the encoded data segment is deemed to havebeen properly stored. The dispersed storage error encoding parametersmay further include slicing information (e.g., the number of encodeddata slices that will be created for each data segment) and/or slicesecurity information (e.g., per encoded data slice encryption,compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1 throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 80 is shown inFIG. 6. As shown, the slice name (SN) 80 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a schematic block diagram of another embodiment of a dispersedstorage network (DSN) that includes a DST processing unit 916, thenetwork 24 of FIG. 1, and a plurality of regions 1-W. Each region caninclude a plurality of cities 1-X, where the number of cities can be thesame or different for each region. Each city can include a plurality ofdata centers 1-Y, where the number of data centers can be the same ordifferent for each city. Each data center can include a plurality ofstorage units 1-Z, each connected to network 24, where the number ofstorage units can be the same or different for each data center. The DSTprocessing unit 916 can include the interface 32 of FIG. 1, thecomputing core 26 of FIG. 1, and the DS client module 34 of FIG. 1. TheDST processing unit 916 can be implemented utilizing a computing device16 of FIG. 1 functioning as a dispersed storage processing agent forcomputing device 14 as described previously. Each storage unit may beimplemented utilizing the storage unit 36 of FIG. 1. The DSN functionsto allocate data for storage by utilizing a location-based hierarchy.

A DSN memory can utilize a hierarchical organization of locations forstoring slices as an alternative to a fully logically organizednamespace. This requires that the product of the number of locationsselected for each level at least equals an Information DispersalAlgorithm (IDA) width, which can include the pillar width discussedpreviously. For example, consider a DSN memory organized utilizing alocation-based hierarchy where, at the top-most level of the hierarchy,are 3 regions, from which at least 2 must be selected: a “Region Width”of 2. Within each of the 3 regions, are 3 to 5 cities, from which atleast 3 must be selected: a “City Width” of 3 Within each of the cityare 2 data centers, from which at least 2 must be selected: a “DataCenter Width” of 2. Within each data center are at least 4 storageunits, with the number selected determined by IDA width: a “storage unitWidth” of 4. In various embodiments, the hierarchy can have any numberof location-based levels. In various embodiments the widths at eachlevel can be received as parameter data and/or can be calculated basedon a deterministic function.

Multiplying each of the widths in this example, the Region Width of 2,the City Width of 3, the Data Center Width of 2, and the ds unit Widthof 4 yields 2*3*2*4=48. Thus, for any possible selection of regions,cities, or data centers, there is a maximum possible selection of 48 dsunits, and IDA width is limited to be less than 48. Note, however, thatother IDA widths are possible, resulting in different numbers of dsunits being selected from each data center. In this example, an IDAwidth of 24 would result in 2 storage units (out of the 4) selected fromeach data center. Selection of the Width number of locations at eachlevel of the hierarchy may be based on a deterministic function, such asa Decentralized Agreement Protocol (DAP). Selection can begin at the topmost level of the hierarchy, with each selection further constrainingand limiting the selection paths for the lower levels. For example,selecting an “East Coast” region might then limited city selection fromamong cities that exist on the east coast. Such constraints can bereceived as parameter data received by the DST processing unit via thenetwork, and can include explicit locations to be included, or weightdata indicating different weights corresponding to the differentlocations, where the storage units are further selected based on theweight data. Once the bottom-most level of the hierarchy is selected,based on the total number of locations selected, and the IDA width, thestorage units can be selected, where number of storage units chosen iscalculated by dividing the IDA Width by the total number of bottom-levellocations selected. For the example above, if 12 data centers areselected, and the IDA width is 36, then the number of storage unitsselected=36/12=3. In various embodiments, the bottom-level of thehierarchy can depend on the IDA width. For example, the bottom-level ofthe hierarchy can end with the cities if the IDA width is small. Forexample, if the IDA width is 6, after selecting 2 regions and 3 citiesfrom each region, one storage unit will be selected from each region,regardless of data center location, because the IDA width of 6 hasalready been achieved. The selection can use a DAP or be logicallyspread out as far as possible over a logical namespace within thatbottom-most level. When changes to the hierarchy occur, such as adding anew city to a region, slices can be remapped within that regionaccording to the deterministic functions applied at the level of citieswithin that region. This can also change the “weight” of that regionwith the DAP at the region level, and result in some cross-regionmovement of slices.

In various embodiments, a processing system of a dispersed storage andtask (DST) processing unit includes at least one processor and a memorythat stores operational instructions, that when executed by the at leastone processor cause the processing system to generate storage allocationdata indicating a first subset of a plurality of storage units based onstorage location hierarchy data and an information dispersal algorithm(IDA) width. A first plurality of write requests corresponding to eachstorage unit in the first subset are generated for transmission via anetwork, where each of the first plurality of write requests includesone of a plurality of encoded slices of a data object.

In various embodiments, the storage location hierarchy data is generatedbased on a decentralized agreement protocol (DAP). In variousembodiments, generating the storage allocation data is further based ona desired number of data locations and a location-based width, whereinthe first subset of the plurality of storage units includes a pluralityof location-based subsets based on the desired number of data locations,each corresponding to a distinct one of a plurality of data locations,wherein the size of each of the plurality of location-based subsets isbased on the location-based width, and wherein each of the plurality oflocation-based subsets includes storage units located at thecorresponding distinct one of the plurality of unique data locations. Invarious embodiments, the location-based width is calculated by dividingthe IDA width by the desired number of data locations. In variousembodiments, the desired number of data locations corresponds to thesize of a set of desired data locations, and wherein the set of desireddata locations includes the plurality of data locations. In variousembodiments, the set of desired data locations includes a plurality ofdata centers.

In various embodiments, the set of desired data locations is selectedbased on location parameter data received via the network. In variousembodiments, the location parameter data includes location weight dataindicating at least one location weight corresponding to at least oneof: at least one region, at least one city, or at least one data center,wherein a distribution of the plurality of data locations in the set ofdesired data locations is based on the location weight data.

In various embodiments, each of the plurality of location-based subsetscorresponds to a distinct one of a plurality of regions, wherein each ofthe plurality of location-based subsets further includes a plurality ofcity-based subsets, each corresponding to a distinct one of a pluralityof cities located in the distinct one of the plurality of regions,wherein each of the plurality of city-based subsets includes a pluralityof center-based subsets, each corresponding to a distinct one of aplurality of data centers located in the distinct one of the pluralityof cities, and wherein the location-based subsets, city-based subsets,and center-based subsets are selected based on the storage locationhierarchy data. In various embodiments, the number of location-basedsubsets is based on region-based width corresponding to a desired numberof regions, wherein the number of city-based subsets in each of theplurality of location-based subsets is based on a city-based widthcorresponding to a desired number of cities in each region, wherein thenumber of center-based subsets in each of the plurality of city-basedsubsets each of the center-based subsets is based on a center-basedwidth corresponding to a desired number of data centers in each city,and wherein the size of each of the plurality of center-based subsets isbased on a storage-unit based width corresponding to a desired number ofstorage units in each data center.

In various embodiments, the desired number of regions, the desirednumber of cities in each region, the desired number of data centers ineach city, and/or the desired number of storage units in each datacenter is calculated based on a decentralized agreement protocol (DAP).In various embodiments, width parameter data is received via the networkthat indicates the desired number of regions, the desired number ofcities in each region, the desired number of data centers in each city,and/or the desired number of storage units in each data center.

In various embodiments, reassignment data is generated in response to achange in the storage location hierarchy data, wherein the reassignmentdata indicating a mapping of the plurality of encoded slices to a newsubset of a second plurality of storage units based on the change in thestorage location hierarchy data. A second plurality of write requestscorresponding to each storage unit in the new subset is generated fortransmission via the network, wherein each of the second plurality ofwrite requests includes one of the plurality of encoded slices of a dataobject based on the reassignment data.

FIG. 10 is a flowchart illustrating an example of allocating data forstorage by utilizing a location-based hierarchy. In particular, a methodis presented for use in association with one or more functions andfeatures described in conjunction with FIGS. 1-9, for execution by adispersed storage and task (DST) processing unit that includes aprocessor or via another processing system of a dispersed storagenetwork that includes at least one processor and memory that storesinstruction that configure the processor or processors to perform thesteps described below. Step 1002 includes generating storage allocationdata indicating a first subset of a plurality of storage units based onstorage location hierarchy data and an information dispersal algorithm(IDA) width. Step 1004 includes generating a first plurality of writerequests corresponding to each storage unit in the first subset fortransmission via a network, where each of the first plurality of writerequests includes one of a plurality of encoded slices of a data object.

In various embodiments, the storage location hierarchy data is generatedbased on a decentralized agreement protocol (DAP). In variousembodiments, generating the storage allocation data is further based ona desired number of data locations and a location-based width, whereinthe first subset of the plurality of storage units includes a pluralityof location-based subsets based on the desired number of data locations,each corresponding to a distinct one of a plurality of data locations,wherein the size of each of the plurality of location-based subsets isbased on the location-based width, and wherein each of the plurality oflocation-based subsets includes storage units located at thecorresponding distinct one of the plurality of unique data locations. Invarious embodiments, the location-based width is calculated by dividingthe IDA width by the desired number of data locations. In variousembodiments, the desired number of data locations corresponds to thesize of a set of desired data locations, and wherein the set of desireddata locations includes the plurality of data locations. In variousembodiments, the set of desired data locations includes a plurality ofdata centers.

In various embodiments, the set of desired data locations is selectedbased on location parameter data received via the network. In variousembodiments, the location parameter data includes location weight dataindicating at least one location weight corresponding to at least oneof: at least one region, at least one city, or at least one data center,wherein a distribution of the plurality of data locations in the set ofdesired data locations is based on the location weight data.

In various embodiments, each of the plurality of location-based subsetscorresponds to a distinct one of a plurality of regions, wherein each ofthe plurality of location-based subsets further includes a plurality ofcity-based subsets, each corresponding to a distinct one of a pluralityof cities located in the distinct one of the plurality of regions,wherein each of the plurality of city-based subsets includes a pluralityof center-based subsets, each corresponding to a distinct one of aplurality of data centers located in the distinct one of the pluralityof cities, and wherein the location-based subsets, city-based subsets,and center-based subsets are selected based on the storage locationhierarchy data. In various embodiments, the number of location-basedsubsets is based on region-based width corresponding to a desired numberof regions, wherein the number of city-based subsets in each of theplurality of location-based subsets is based on a city-based widthcorresponding to a desired number of cities in each region, wherein thenumber of center-based subsets in each of the plurality of city-basedsubsets each of the center-based subsets is based on a center-basedwidth corresponding to a desired number of data centers in each city,and wherein the size of each of the plurality of center-based subsets isbased on a storage-unit based width corresponding to a desired number ofstorage units in each data center.

In various embodiments, the desired number of regions, the desirednumber of cities in each region, the desired number of data centers ineach city, and/or the desired number of storage units in each datacenter is calculated based on a decentralized agreement protocol (DAP).In various embodiments, width parameter data is received via the networkthat indicates the desired number of regions, the desired number ofcities in each region, the desired number of data centers in each city,and/or the desired number of storage units in each data center.

In various embodiments, reassignment data is generated in response to achange in the storage location hierarchy data, wherein the reassignmentdata indicating a mapping of the plurality of encoded slices to a newsubset of a second plurality of storage units based on the change in thestorage location hierarchy data. A second plurality of write requestscorresponding to each storage unit in the new subset is generated fortransmission via the network, wherein each of the second plurality ofwrite requests includes one of the plurality of encoded slices of a dataobject based on the reassignment data.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to generate storage allocation data indicating a firstsubset of a plurality of storage units based on storage locationhierarchy data and an information dispersal algorithm (IDA) width. Afirst plurality of write requests corresponding to each storage unit inthe first subset are generated for transmission via a network, whereeach of the first plurality of write requests includes one of aplurality of encoded slices of a data object.

FIG. 11 is a schematic block diagram of an embodiment of a decentralizedagreement module 350 that includes a set of deterministic functions 1-N,a set of normalizing functions 1-N, a set of scoring functions 1-N, anda ranking function 352. Each of the deterministic function, thenormalizing function, the scoring function, and the ranking function352, may be implemented utilizing the computing core 26 of FIG. 2. Thedecentralized agreement module 350 may be implemented utilizing anymodule and/or unit of a dispersed storage network (DSN). For example,the decentralized agreement module is implemented utilizing thedistributed storage and task (DST) client module 34 of FIG. 1. Invarious embodiments, storage units will be selected according to thehierarchical structure described previously by utilizing thedecentralized agreement module.

The decentralized agreement module 350 functions to receive a rankedscoring information request 354 and to generate ranked scoringinformation 358 based on the ranked scoring information request 354 andother information. The ranked scoring information request 354 includesone or more of an asset identifier (ID) 356 of an asset associated withthe request, an asset type indicator, one or more location identifiersof locations associated with the DSN, one or more corresponding locationweights, and a requesting entity ID. The asset includes any portion ofdata associated with the DSN including one or more asset types includinga data object, a data record, an encoded data slice, a data segment, aset of encoded data slices, and a plurality of sets of encoded dataslices. As such, the asset ID 356 of the asset includes one or more of adata name, a data record identifier, a source name, a slice name, and aplurality of sets of slice names.

Each location of the DSN includes an aspect of a DSN resource. Examplesof locations includes one or more of a storage unit, a memory device ofthe storage unit, a site, a storage pool of storage units, a pillarindex associated with each encoded data slice of a set of encoded dataslices generated by an information dispersal algorithm (IDA), a DSclient module 34 of FIG. 1, a computing device 12-16 of FIG. 1, anintegrity unit 20 of FIG. 1, and a managing unit 18 of FIG. 1.

Each location is associated with a location weight based on one or moreof a resource prioritization of utilization scheme and physicalconfiguration of the DSN. The location weight includes an arbitrary biaswhich adjusts a proportion of selections to an associated location suchthat a probability that an asset will be mapped to that location isequal to the location weight divided by a sum of all location weightsfor all locations of comparison. For example, each storage pool of aplurality of storage pools is associated with a location weight based onstorage capacity. For instance, storage pools with more storage capacityare associated with higher location weights than others. The otherinformation may include a set of location identifiers and a set oflocation weights associated with the set of location identifiers. Forexample, the other information includes location identifiers andlocation weights associated with a set of memory devices of a storageunit when the requesting entity utilizes the decentralized agreementmodule 350 to produce ranked scoring information 358 with regards toselection of a memory device of the set of memory devices for accessinga particular encoded data slice (e.g., where the asset ID includes aslice name of the particular encoded data slice).

The decentralized agreement module 350 outputs substantially identicalranked scoring information for each ranked scoring information requestthat includes substantially identical content of the ranked scoringinformation request. For example, a first requesting entity issues afirst ranked scoring information request to the decentralized agreementmodule 350 and receives first ranked scoring information. A secondrequesting entity issues a second ranked scoring information request tothe decentralized agreement module and receives second ranked scoringinformation. The second ranked scoring information is substantially thesame as the first ranked scoring information when the second rankedscoring information request is substantially the same as the firstranked scoring information request.

As such, two or more requesting entities may utilize the decentralizedagreement module 350 to determine substantially identical ranked scoringinformation. As a specific example, the first requesting entity selectsa first storage pool of a plurality of storage pools for storing a setof encoded data slices utilizing the decentralized agreement module 350and the second requesting entity identifies the first storage pool ofthe plurality of storage pools for retrieving the set of encoded dataslices utilizing the decentralized agreement module 350.

In an example of operation, the decentralized agreement module 350receives the ranked scoring information request 354. Each deterministicfunction performs a deterministic function on a combination and/orconcatenation (e.g., add, append, interleave) of the asset ID 356 of theranked scoring information request 354 and an associated location ID ofthe set of location IDs to produce an interim result. The deterministicfunction includes at least one of a hashing function, a hash-basedmessage authentication code function, a mask generating function, acyclic redundancy code function, hashing module of a number oflocations, consistent hashing, rendezvous hashing, and a spongefunction. As a specific example, deterministic function 2 appends alocation ID 2 of a storage pool 2 to a source name as the asset ID toproduce a combined value and performs the mask generating function onthe combined value to produce interim result 2.

With a set of interim results 1-N, each normalizing function performs anormalizing function on a corresponding interim result to produce acorresponding normalized interim result. The performing of thenormalizing function includes dividing the interim result by a number ofpossible permutations of the output of the deterministic function toproduce the normalized interim result. For example, normalizing function2 performs the normalizing function on the interim result 2 to produce anormalized interim result 2.

With a set of normalized interim results 1-N, each scoring functionperforms a scoring function on a corresponding normalized interim resultto produce a corresponding score. The performing of the scoring functionincludes dividing an associated location weight by a negative log of thenormalized interim result. For example, scoring function 2 divideslocation weight 2 of the storage pool 2 (e.g., associated with locationID 2) by a negative log of the normalized interim result 2 to produce ascore 2.

With a set of scores 1-N, the ranking function 352 performs a rankingfunction on the set of scores 1-N to generate the ranked scoringinformation 358. The ranking function includes rank ordering each scorewith other scores of the set of scores 1-N, where a highest score isranked first. As such, a location associated with the highest score maybe considered a highest priority location for resource utilization(e.g., accessing, storing, retrieving, etc., the given asset of therequest). Having generated the ranked scoring information 358, thedecentralized agreement module 350 outputs the ranked scoringinformation 358 to the requesting entity.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or maybe distributed located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a dispersed storage andtask (DST) processing unit that includes a processor, the methodcomprises: generating storage allocation data indicating a first subsetof a plurality of storage units based on storage location hierarchy dataand an information dispersal algorithm (IDA) width; and generating afirst plurality of write requests corresponding to each storage unit inthe first subset for transmission via a network, wherein each of thefirst plurality of write requests includes one of a plurality of encodedslices of a data object.
 2. The method of claim 1, further comprisinggenerating the storage location hierarchy data based on a decentralizedagreement protocol (DAP).
 3. The method of claim 1, wherein generatingthe storage allocation data is further based on a desired number of datalocations and a location-based width, wherein the first subset of theplurality of storage units includes a plurality of location-basedsubsets based on the desired number of data locations, eachcorresponding to a distinct one of a plurality of data locations,wherein the size of each of the plurality of location-based subsets isbased on the location-based width, and wherein each of the plurality oflocation-based subsets includes storage units located at thecorresponding distinct one of the plurality of unique data locations. 4.The method of claim 3, further comprising calculating the location-basedwidth by dividing the IDA width by the desired number of data locations.5. The method of claim 3, wherein the desired number of data locationscorresponds to the size of a set of desired data locations, and whereinthe set of desired data locations includes the plurality of datalocations.
 6. The method of claim 5, wherein the set of desired datalocations includes a plurality of data centers.
 7. The method of claim5, further comprising selecting the set of desired data locations basedon location parameter data received via the network.
 8. The method ofclaim 7, wherein the location parameter data includes location weightdata indicating at least one location weight corresponding to at leastone of: at least one region, at least one city, or at least one datacenter, wherein a distribution of the plurality of data locations in theset of desired data locations is based on the location weight data. 9.The method of claim 3, wherein each of the plurality of location-basedsubsets corresponds to a distinct one of a plurality of regions, whereineach of the plurality of location-based subsets further includes aplurality of city-based subsets, each corresponding to a distinct one ofa plurality of cities located in the distinct one of the plurality ofregions, wherein each of the plurality of city-based subsets includes aplurality of center-based subsets, each corresponding to a distinct oneof a plurality of data centers located in the distinct one of theplurality of cities, and wherein the location-based subsets, city-basedsubsets, and center-based subsets are selected based on the storagelocation hierarchy data.
 10. The method of claim 9, wherein the numberof location-based subsets is based on region-based width correspondingto a desired number of regions, wherein the number of city-based subsetsin each of the plurality of location-based subsets is based on acity-based width corresponding to a desired number of cities in eachregion, wherein the number of center-based subsets in each of theplurality of city-based subsets each of the center-based subsets isbased on a center-based width corresponding to a desired number of datacenters in each city, and wherein the size of each of the plurality ofcenter-based subsets is based on a storage-unit based widthcorresponding to a desired number of storage units in each data center.11. The method of claim 10, wherein at least one of: the desired numberof regions, the desired number of cities in each region, the desirednumber of data centers in each city, or the desired number of storageunits in each data center is calculated based on a decentralizedagreement protocol (DAP).
 12. The method of claim 10, further comprisingreceiving width parameter data via the network that indicates at leastone of: the desired number of regions, the desired number of cities ineach region, the desired number of data centers in each city, or thedesired number of storage units in each data center.
 13. The method ofclaim 10, wherein the product of the desired number of regions, thedesired number of cities in each region, the desired number of datacenters in each city, and the desired number of storage units in eachdata center is greater than or equal to the IDA width.
 14. The method ofclaim 1, further comprising: generating reassignment data in response toa change in the storage location hierarchy data, wherein thereassignment data indicating a mapping of the plurality of encodedslices to a new subset of a second plurality of storage units based onthe change in the storage location hierarchy data; and generating asecond plurality of write requests corresponding to each storage unit inthe new subset for transmission via the network, wherein each of thesecond plurality of write requests includes one of the plurality ofencoded slices of a data object based on the reassignment data.
 15. Aprocessing system of a dispersed storage and task (DST) processing unitcomprises: at least one processor; a memory that stores operationalinstructions, that when executed by the at least one processor cause theprocessing system to: generate storage allocation data indicating afirst subset of a plurality of storage units based on storage locationhierarchy data and an information dispersal algorithm (IDA) width; andgenerate a first plurality of write requests corresponding to eachstorage unit in the first subset for transmission via a network, whereineach of the first plurality of write requests includes one of aplurality of encoded slices of a data object.
 16. The processing systemof claim 15, wherein the operational instructions, when executed by theat least one processor, further cause the processing system to generatethe storage location hierarchy data based on a decentralized agreementprotocol (DAP).
 17. The processing system of claim 15, whereingenerating the storage allocation data is further based on a desirednumber of data locations and a location-based width, wherein the firstsubset of the plurality of storage units includes a plurality oflocation-based subsets based on the desired number of data locations,each corresponding to a distinct one of a plurality of data locations,wherein the size of each of the plurality of location-based subsets isbased on the location-based width, and wherein each of the plurality oflocation-based subsets includes storage units located at thecorresponding distinct one of the plurality of unique data locations.18. The processing system of claim 17, wherein the operationalinstructions, when executed by the at least one processor, further causethe processing system to calculate the location-based width by dividingthe IDA width by the desired number of data locations.
 19. Theprocessing system of claim 17, wherein each of the plurality oflocation-based subsets corresponds to a distinct one of a plurality ofregions, wherein each of the plurality of location-based subsets furtherincludes a plurality of city-based subsets, each corresponding to adistinct one of a plurality of cities located in the distinct one of theplurality of regions, wherein each of the plurality of city-basedsubsets includes a plurality of center-based subsets, each correspondingto a distinct one of a plurality of data centers located in the distinctone of the plurality of cities, and wherein the location-based subsets,city-based subsets, and center-based subsets are selected based on thestorage location hierarchy data.
 20. A non-transitory computer readablestorage medium comprises: at least one memory section that storesoperational instructions that, when executed by a processing system of adispersed storage network (DSN) that includes a processor and a memory,causes the processing system to: generate storage allocation dataindicating a first subset of a plurality of storage units based onstorage location hierarchy data and an information dispersal algorithm(IDA) width; and generate a first plurality of write requestscorresponding to each storage unit in the first subset for transmissionvia a network, wherein each of the first plurality of write requestsincludes one of a plurality of encoded slices of a data object.